{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 多优化器前馈神经网络手写数字识别实验"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 环境设置与依赖导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim.optimizer import Optimizer\n",
    "import torchvision\n",
    "from torch.utils.data import DataLoader, Subset\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import Tuple, Dict, List, Type\n",
    "\n",
    "# 设置绘图字体为黑体\n",
    "plt.rcParams[\"font.family\"] = \"SimHei\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 可复现性设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 固定随机种子保证可复现\n",
    "torch.manual_seed(42)\n",
    "torch.backends.cudnn.deterministic = True\n",
    "torch.backends.cudnn.benchmark = False\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 设备检测函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_optimal_device_name() -> str:\n",
    "    \"\"\"自动检测最优计算设备\"\"\"\n",
    "    if torch.cuda.is_available():\n",
    "        return \"cuda\"\n",
    "    try:\n",
    "        import torch_directml  # type: ignore\n",
    "        return torch_directml.device()\n",
    "    except Exception:\n",
    "        pass\n",
    "    return \"cpu\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 实验参数配置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当前使用设备: privateuseone:0\n"
     ]
    }
   ],
   "source": [
    "class Config:\n",
    "    # 数据参数\n",
    "    train_size = 5000\n",
    "    test_size = 1000\n",
    "    batch_size = 64\n",
    "    num_workers = 2\n",
    "\n",
    "    # 训练参数\n",
    "    epochs = 10\n",
    "    learning_rate = 0.001\n",
    "\n",
    "    # 设备配置\n",
    "    device = get_optimal_device_name()\n",
    "\n",
    "# 打印设备信息\n",
    "print(f\"当前使用设备: {Config.device}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 网络结构定义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ConvNet(\n",
      "  (conv_block1): Sequential(\n",
      "    (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n",
      "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU()\n",
      "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (conv_block2): Sequential(\n",
      "    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU()\n",
      "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (fc): Sequential(\n",
      "    (0): Linear(in_features=1600, out_features=128, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Linear(in_features=128, out_features=10, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class ConvNet(nn.Module):\n",
    "    \"\"\"卷积神经网络模型\"\"\"\n",
    "    def __init__(self):\n",
    "        super(ConvNet, self).__init__()\n",
    "        # 卷积块1\n",
    "        self.conv_block1 = nn.Sequential(\n",
    "            nn.Conv2d(1, 32, kernel_size=3),  # 输出: (32, 26, 26)\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2),  # 输出: (32, 13, 13)\n",
    "        )\n",
    "\n",
    "        # 卷积块2\n",
    "        self.conv_block2 = nn.Sequential(\n",
    "            nn.Conv2d(32, 64, kernel_size=3),  # 输出: (64, 11, 11)\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2),  # 输出: (64, 5, 5)\n",
    "        )\n",
    "\n",
    "        # 全连接层\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(64 * 5 * 5, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128, 10)\n",
    "        )\n",
    "\n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        x = self.conv_block1(x)\n",
    "        x = self.conv_block2(x)\n",
    "        x = x.view(x.size(0), -1)  # 展平\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "# 打印模型结构\n",
    "model = ConvNet()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_data() -> Tuple[DataLoader, DataLoader]:\n",
    "    \"\"\"准备训练和测试数据集\"\"\"\n",
    "    # 下载完整数据集\n",
    "    transform = torchvision.transforms.Compose([\n",
    "        torchvision.transforms.ToTensor(),\n",
    "        torchvision.transforms.Normalize((0.1307,), (0.3081,))\n",
    "    ])\n",
    "    \n",
    "    train_full = torchvision.datasets.MNIST(\n",
    "        root='./data', train=True, download=True, transform=transform)\n",
    "    \n",
    "    test_full = torchvision.datasets.MNIST(\n",
    "        root='./data', train=False, download=True, transform=transform)\n",
    "    \n",
    "    # 创建子集\n",
    "    train_indices = torch.randperm(len(train_full))[:Config.train_size]\n",
    "    test_indices = torch.randperm(len(test_full))[:Config.test_size]\n",
    "    \n",
    "    train_loader = DataLoader(\n",
    "        Subset(train_full, train_indices.tolist()),\n",
    "        batch_size=Config.batch_size,\n",
    "        shuffle=True,\n",
    "        num_workers=Config.num_workers)\n",
    "    \n",
    "    test_loader = DataLoader(\n",
    "        Subset(test_full, test_indices.tolist()),\n",
    "        batch_size=Config.batch_size,\n",
    "        shuffle=False,\n",
    "        num_workers=Config.num_workers)\n",
    "    \n",
    "    print(f\"训练集大小: {len(train_loader.dataset)} 样本\")\n",
    "    print(f\"测试集大小: {len(test_loader.dataset)} 样本\")\n",
    "    return train_loader, test_loader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 训练与测试循环"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_loop(\n",
    "    optimizer_type: Type[Optimizer],\n",
    "    train_loader: DataLoader,\n",
    "    test_loader: DataLoader,\n",
    ") -> Tuple[List[float], List[float], List[float], List[float]]:\n",
    "    \"\"\"训练和测试循环\"\"\"\n",
    "    # 初始化模型和损失函数\n",
    "    model = ConvNet().to(Config.device)\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "    # 选择优化器\n",
    "    optimizer = optimizer_type(model.parameters(), lr=Config.learning_rate)\n",
    "\n",
    "    # 记录指标\n",
    "    train_losses, train_accs = [], []\n",
    "    test_losses, test_accs = [], []\n",
    "\n",
    "    for epoch in range(Config.epochs):\n",
    "        # 训练阶段\n",
    "        model.train()\n",
    "        running_loss, correct, total = 0.0, 0, 0\n",
    "\n",
    "        for images, labels in train_loader:\n",
    "            images, labels = images.to(Config.device), labels.to(Config.device)\n",
    "            \n",
    "            # 前向传播\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            \n",
    "            # 反向传播和优化\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            # 统计信息\n",
    "            running_loss += loss.item()\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "\n",
    "        # 计算训练指标\n",
    "        train_loss = running_loss / len(train_loader)\n",
    "        train_acc = correct / total\n",
    "        train_losses.append(train_loss)\n",
    "        train_accs.append(train_acc)\n",
    "\n",
    "        # 测试阶段\n",
    "        model.eval()\n",
    "        test_loss, correct, total = 0.0, 0, 0\n",
    "\n",
    "        with torch.no_grad():\n",
    "            for images, labels in test_loader:\n",
    "                images, labels = images.to(Config.device), labels.to(Config.device)\n",
    "                outputs = model(images)\n",
    "                loss = criterion(outputs, labels)\n",
    "                \n",
    "                test_loss += loss.item()\n",
    "                _, predicted = torch.max(outputs.data, 1)\n",
    "                total += labels.size(0)\n",
    "                correct += (predicted == labels).sum().item()\n",
    "\n",
    "        # 计算测试指标\n",
    "        test_loss = test_loss / len(test_loader)\n",
    "        test_acc = correct / total\n",
    "        test_losses.append(test_loss)\n",
    "        test_accs.append(test_acc)\n",
    "\n",
    "        print(f\"优化器 {optimizer_type.__name__} Epoch [{epoch+1}/{Config.epochs}]\")\n",
    "        print(f\"训练损失: {train_loss:.4f} 准确率: {train_acc:.4f}\")\n",
    "        print(f\"测试损失: {test_loss:.4f} 准确率: {test_acc:.4f}\\n\")\n",
    "\n",
    "    return train_losses, train_accs, test_losses, test_accs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. 结果可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_results(results: Dict[str, Tuple[List[float], ...]]):\n",
    "    \"\"\"绘制训练结果曲线\"\"\"\n",
    "    plt.figure(figsize=(12, 8))\n",
    "\n",
    "    # 准确率曲线\n",
    "    plt.subplot(2, 1, 1)\n",
    "    for opt_name, (_, train_accs, _, test_accs) in results.items():\n",
    "        plt.plot(train_accs, label=f\"{opt_name} (Train)\")\n",
    "        plt.plot(test_accs, \"--\", label=f\"{opt_name} (Test)\")\n",
    "    plt.title(\"准确率曲线\")\n",
    "    plt.ylabel(\"Accuracy\")\n",
    "    plt.legend()\n",
    "\n",
    "    # 损失曲线\n",
    "    plt.subplot(2, 1, 2)\n",
    "    for opt_name, (train_losses, _, test_losses, _) in results.items():\n",
    "        plt.plot(train_losses, label=f\"{opt_name} (Train)\")\n",
    "        plt.plot(test_losses, \"--\", label=f\"{opt_name} (Test)\")\n",
    "    plt.title(\"损失曲线\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.xlabel(\"Epoch\")\n",
    "    plt.legend()\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(\"results.png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. 主实验流程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集大小: 5000 样本\n",
      "测试集大小: 1000 样本\n",
      "\n",
      "============================== 正在训练优化器: SGD ==============================\n",
      "优化器 SGD Epoch [1/10]\n",
      "训练损失: 2.1597 准确率: 0.3170\n",
      "测试损失: 2.0286 准确率: 0.4910\n",
      "\n",
      "优化器 SGD Epoch [2/10]\n",
      "训练损失: 1.8931 准确率: 0.6096\n",
      "测试损失: 1.7908 准确率: 0.6630\n",
      "\n",
      "优化器 SGD Epoch [3/10]\n",
      "训练损失: 1.6507 准确率: 0.7370\n",
      "测试损失: 1.5555 准确率: 0.7680\n",
      "\n",
      "优化器 SGD Epoch [4/10]\n",
      "训练损失: 1.4237 准确率: 0.7988\n",
      "测试损失: 1.3575 准确率: 0.7940\n",
      "\n",
      "优化器 SGD Epoch [5/10]\n",
      "训练损失: 1.2239 准确率: 0.8264\n",
      "测试损失: 1.1696 准确率: 0.8320\n",
      "\n",
      "优化器 SGD Epoch [6/10]\n",
      "训练损失: 1.0609 准确率: 0.8516\n",
      "测试损失: 1.0283 准确率: 0.8500\n",
      "\n",
      "优化器 SGD Epoch [7/10]\n",
      "训练损失: 0.9208 准确率: 0.8660\n",
      "测试损失: 0.9013 准确率: 0.8590\n",
      "\n",
      "优化器 SGD Epoch [8/10]\n",
      "训练损失: 0.8143 准确率: 0.8774\n",
      "测试损失: 0.8048 准确率: 0.8680\n",
      "\n",
      "优化器 SGD Epoch [9/10]\n",
      "训练损失: 0.7245 准确率: 0.8864\n",
      "测试损失: 0.7278 准确率: 0.8820\n",
      "\n",
      "优化器 SGD Epoch [10/10]\n",
      "训练损失: 0.6542 准确率: 0.8940\n",
      "测试损失: 0.6617 准确率: 0.8840\n",
      "\n",
      "\n",
      "============================== 正在训练优化器: Adagrad ==============================\n",
      "优化器 Adagrad Epoch [1/10]\n",
      "训练损失: 0.7537 准确率: 0.8366\n",
      "测试损失: 0.3890 准确率: 0.9240\n",
      "\n",
      "优化器 Adagrad Epoch [2/10]\n",
      "训练损失: 0.2818 准确率: 0.9402\n",
      "测试损失: 0.3066 准确率: 0.9320\n",
      "\n",
      "优化器 Adagrad Epoch [3/10]\n",
      "训练损失: 0.2042 准确率: 0.9562\n",
      "测试损失: 0.2379 准确率: 0.9410\n",
      "\n",
      "优化器 Adagrad Epoch [4/10]\n",
      "训练损失: 0.1670 准确率: 0.9610\n",
      "测试损失: 0.1978 准确率: 0.9470\n",
      "\n",
      "优化器 Adagrad Epoch [5/10]\n",
      "训练损失: 0.1477 准确率: 0.9702\n",
      "测试损失: 0.1808 准确率: 0.9530\n",
      "\n",
      "优化器 Adagrad Epoch [6/10]\n",
      "训练损失: 0.1292 准确率: 0.9704\n",
      "测试损失: 0.1682 准确率: 0.9570\n",
      "\n",
      "优化器 Adagrad Epoch [7/10]\n",
      "训练损失: 0.1188 准确率: 0.9754\n",
      "测试损失: 0.1622 准确率: 0.9550\n",
      "\n",
      "优化器 Adagrad Epoch [8/10]\n",
      "训练损失: 0.1077 准确率: 0.9772\n",
      "测试损失: 0.1501 准确率: 0.9580\n",
      "\n",
      "优化器 Adagrad Epoch [9/10]\n",
      "训练损失: 0.0996 准确率: 0.9796\n",
      "测试损失: 0.1450 准确率: 0.9650\n",
      "\n",
      "优化器 Adagrad Epoch [10/10]\n",
      "训练损失: 0.0931 准确率: 0.9800\n",
      "测试损失: 0.1391 准确率: 0.9660\n",
      "\n",
      "\n",
      "============================== 正在训练优化器: RMSProp ==============================\n",
      "优化器 RMSprop Epoch [1/10]\n",
      "训练损失: 0.9225 准确率: 0.7990\n",
      "测试损失: 0.2435 准确率: 0.9270\n",
      "\n",
      "优化器 RMSprop Epoch [2/10]\n",
      "训练损失: 0.1349 准确率: 0.9604\n",
      "测试损失: 0.1269 准确率: 0.9570\n",
      "\n",
      "优化器 RMSprop Epoch [3/10]\n",
      "训练损失: 0.0850 准确率: 0.9732\n",
      "测试损失: 0.1153 准确率: 0.9620\n",
      "\n",
      "优化器 RMSprop Epoch [4/10]\n",
      "训练损失: 0.0587 准确率: 0.9818\n",
      "测试损失: 0.1011 准确率: 0.9670\n",
      "\n",
      "优化器 RMSprop Epoch [5/10]\n",
      "训练损失: 0.0453 准确率: 0.9884\n",
      "测试损失: 1.5459 准确率: 0.6180\n",
      "\n",
      "优化器 RMSprop Epoch [6/10]\n",
      "训练损失: 0.0592 准确率: 0.9844\n",
      "测试损失: 0.2092 准确率: 0.9330\n",
      "\n",
      "优化器 RMSprop Epoch [7/10]\n",
      "训练损失: 0.0218 准确率: 0.9938\n",
      "测试损失: 0.0863 准确率: 0.9710\n",
      "\n",
      "优化器 RMSprop Epoch [8/10]\n",
      "训练损失: 0.0120 准确率: 0.9974\n",
      "测试损失: 0.0809 准确率: 0.9770\n",
      "\n",
      "优化器 RMSprop Epoch [9/10]\n",
      "训练损失: 0.0196 准确率: 0.9944\n",
      "测试损失: 0.2562 准确率: 0.9380\n",
      "\n",
      "优化器 RMSprop Epoch [10/10]\n",
      "训练损失: 0.0141 准确率: 0.9962\n",
      "测试损失: 0.1003 准确率: 0.9690\n",
      "\n",
      "\n",
      "============================== 正在训练优化器: Adam ==============================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\19601\\AppData\\Roaming\\Python\\Python312\\site-packages\\torch\\optim\\adam.py:534: UserWarning: The operator 'aten::lerp.Scalar_out' is not currently supported on the DML backend and will fall back to run on the CPU. This may have performance implications. (Triggered internally at C:\\__w\\1\\s\\pytorch-directml-plugin\\torch_directml\\csrc\\dml\\dml_cpu_fallback.cpp:17.)\n",
      "  torch._foreach_lerp_(device_exp_avgs, device_grads, 1 - beta1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "优化器 Adam Epoch [1/10]\n",
      "训练损失: 0.4552 准确率: 0.8636\n",
      "测试损失: 0.1828 准确率: 0.9430\n",
      "\n",
      "优化器 Adam Epoch [2/10]\n",
      "训练损失: 0.1007 准确率: 0.9708\n",
      "测试损失: 0.1078 准确率: 0.9650\n",
      "\n",
      "优化器 Adam Epoch [3/10]\n",
      "训练损失: 0.0653 准确率: 0.9822\n",
      "测试损失: 0.0985 准确率: 0.9740\n",
      "\n",
      "优化器 Adam Epoch [4/10]\n",
      "训练损失: 0.0450 准确率: 0.9864\n",
      "测试损失: 0.0913 准确率: 0.9680\n",
      "\n",
      "优化器 Adam Epoch [5/10]\n",
      "训练损失: 0.0318 准确率: 0.9912\n",
      "测试损失: 0.0944 准确率: 0.9690\n",
      "\n",
      "优化器 Adam Epoch [6/10]\n",
      "训练损失: 0.0337 准确率: 0.9898\n",
      "测试损失: 0.0885 准确率: 0.9740\n",
      "\n",
      "优化器 Adam Epoch [7/10]\n",
      "训练损失: 0.0136 准确率: 0.9962\n",
      "测试损失: 0.0914 准确率: 0.9750\n",
      "\n",
      "优化器 Adam Epoch [8/10]\n",
      "训练损失: 0.0073 准确率: 0.9992\n",
      "测试损失: 0.1109 准确率: 0.9710\n",
      "\n",
      "优化器 Adam Epoch [9/10]\n",
      "训练损失: 0.0044 准确率: 0.9990\n",
      "测试损失: 0.0697 准确率: 0.9780\n",
      "\n",
      "优化器 Adam Epoch [10/10]\n",
      "训练损失: 0.0020 准确率: 1.0000\n",
      "测试损失: 0.0630 准确率: 0.9810\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "最终测试准确率:\n",
      "SGD: 0.8840\n",
      "Adagrad: 0.9660\n",
      "RMSProp: 0.9690\n",
      "Adam: 0.9810\n"
     ]
    }
   ],
   "source": [
    "# 准备数据\n",
    "train_loader, test_loader = prepare_data()\n",
    "\n",
    "# 优化器列表\n",
    "optimizers = {\n",
    "    \"SGD\": optim.SGD,\n",
    "    \"Adagrad\": optim.Adagrad,\n",
    "    \"RMSProp\": optim.RMSprop,\n",
    "    \"Adam\": optim.Adam,\n",
    "}\n",
    "\n",
    "# 存储结果\n",
    "results = {}\n",
    "\n",
    "# 遍历优化器进行实验\n",
    "for opt_name, opt_class in optimizers.items():\n",
    "    print(f\"\\n{'='*30} 正在训练优化器: {opt_name} {'='*30}\")\n",
    "    metrics = train_test_loop(opt_class, train_loader, test_loader)\n",
    "    results[opt_name] = metrics\n",
    "\n",
    "# 可视化结果\n",
    "plot_results(results)\n",
    "\n",
    "# 输出最终测试准确率\n",
    "print(\"\\n最终测试准确率:\")\n",
    "for opt_name, metrics in results.items():\n",
    "    print(f\"{opt_name}: {metrics[3][-1]:.4f}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
